The present invention in its most preferred embodiments relates to the detection of leaks in boiler tubes; more particularly it relates to the early detection of leaks in tubes of industrial type boilers to thereby allow the operators of such boilers, including utilities, to schedule a shut down for repair rather than suffer a forced outage when such leaks later become catastrophic; and still more particularly to such early detection of leaks to thereby significantly increase the chances of limiting damage to adjacent tubes in such boilers. The present new and improved system utilizes an approach different from those heretofore taken and taught in the prior art. As utilized herein, there is effected the monitoring of a set of tube leak sensitive variables, i.e. variables which exhibit significant changes whenever a leak occurs in a boiler tube. It will be appreciated, of course, that when a tube starts to leak, the output values of these sensitive variables start to change in response to that particular leak. In addition, in the approach utilized in the development of the instant invention including the methods, techniques, and system comprising same, the approach has been to correlate more than one sensitive variable to such leak. Accordingly, by relying on different sources of information about a leak and by correlating a number of leak sensitive variables, it has been found that the likelihood of early detection of such leaks is greatly enhanced. As will be appreciated from a more detailed description infra, in the technique comprising the instant invention, one of the first tasks was to find a functional map between the changes in a plurality of such sensitive variables and the occurrence of a tube leak, i.e. a multi-variable function whose parameters are the sensitive variables and whose output is the tube leak level. Of course, classical approximation methods might be used to find this map as, for instance, by using the Weierstrauss theorem wherein a continuous function can be approximated to an arbitrary degree of accuracy through the utilization of classical techniques employing, for instance, a polynomial. However, for the very complex situation related to tube leaks in boilers, there are several reasons why such classical approximation methods are not suitable including, for instance, that such technique requires one to assume a priori form of the map, i.e. the degree of the polynomial in order to approximate same. A further reason why such approximation methods are not suitable is that extensive computer simulations have shown that for high order polynomials, which would be the case in the present invention arena, the approximation of complex maps results in numerical instabilities which are encountered during the computation of the coefficients of the polynomial. Still another reason for not using such a classical approach is that it is fraught with the difficulties of being not easily implemented in computer hardware. On the other hand, the instant invention, in its simpler form, utilizes artificial neural networks (ANN) to identify the complex map. Since ANNs are known to be model free approximaters one does not need to assume a priori form of the map and further computer simulations are easily and effectively utilized in both computer hardware and software. Accordingly, the instant invention relates to the utilization of a plurality of ANNs to detect the presence of a tube leak as well as determine its location in the boiler. Further, the instant technique utilizes a decentralized architecture or structure for such networks. More specifically, a first ANN is utilized to make a relatively simple decision concerning the presence of a leak. This first ANN is trained on what is herein referred to as universal leak sensitive variables (ULSV), which are sensitive variables that respond to a leak in a boiler regardless of its location therein. Once such first ANN determines that there is indeed a leak in the boiler, the next process step in the practice of the instant invention is to utilize what is herein referred to as local leak sensitive variables (LLSV), rather than said ULSVs, which LLSVs are most sensitive for a given location both along a tube and across the cross section of the boiler. It has been found that there are a plurality of common sensitive variables for designated locations in a boiler and the present invention utilizes the most sensitive thereof for a given location wherein the presence of the leak is manifested by a change in the same subset of such LLSVs. Accordingly, a plurality of dedicated ANNS are utilized in this second step to perform localized leak detection for the location of such common sensitive variables. Although ANNs are known to be universal approximaters, they utilize data driven approaches which translates into performance acceptable for boilers having similar characteristics. In other words, an ANN based system although quite improved over heretofore prior art methods for early detection of tubes requires that after it is trained it be utilized only on similar type boilers. Since a principle object of the instant invention is, at least in the more sophisticated embodiments thereof, to provide a high degree of portability of the instant system wherein is required a minimum of tuning of same when it is used and moved from one boiler to another, the more advanced embodiments of this invention utilize the integration of fuzzy logic with such ANNs whereby is utilized available input-output information about tube leaks to build a fuzzy map whose input is available, numerical, and linguistic tube leak information and whose output is characterization of the sensitive variables. In this more sophisticated approach, there is utilized inference engines to invert the resulting map and to render more accurate decisions about tube leaks in boilers. The decision making procedure utilized in the operation in these more sophisticated integrated systems has been found to be greatly implemented by the use of a set of xe2x80x9cIf Thenxe2x80x9d rules.
The present invention relates generally to new, improved, and reliable systems, methods, and techniques for the detection of leaks in the tubes of industrial boilers, including those of the types used by utilities to produce steam for electric power production.
Boiler Tube Leak Detection. Because of heat, pressure, and wear over time, boiler tubes eventually begin to leak, i.e., the beginning of a xe2x80x9cleak event.xe2x80x9d When a boiler tube(s) starts to leak, steam which flashes over from the water escaping through the leak therein is lost to the boiler environment. In general, the amount of leaked water/steam may be small at the inception of a tube leak event. However, unless the tube is repaired, the leak will continue to grow, i.e., the tube leak rate increases with time until the tube eventually ruptures. Once such rupture occurs the utility operating such boiler is forced to shut it down immediately.
Boiler tube failures are a major cause of forced shut downs in fossil power plants. For example, approximately 41,000 tube failures occur every year in the United States alone. The cost of these failures proves to be quite expensive for utilities, exceeding $5 billion a year. [Lind, M. H., xe2x80x9cBoiler Tube Leak Detection System,xe2x80x9d Proceedings of the Third EPRI Incipient-Failure Detection Conference, EPRI CS-5395, Mar. 1987]. In order to reduce the occurrences of such forced outages, early boiler tube leak detection is highly desirable. Early boiler tube leak detection would allow utilities to schedule a repair rather than to suffer a later forced outage. In addition, the earlier the detection, the better the chances are of limiting damage to adjacent tubes.
Artificial Neural Networks. Artificial neural networks (ANNs) are information-processing models inspired by the architecture of the human brain. ANNs are capable of learning and generalization and are model-free adaptive estimators of maps (relations between the input and the output of the ANN, or, as later referenced, an inference engine) which learn using example data. As is discussed in the prior art, including the patent literature, when a neural network is to be used in detection applications, it is necessary to execute beforehand a learning procedure for establishing suitable parameter values within the ANN. In the learning procedure, a set of sample patterns (referred to herein as the learning data), which have been selected in accordance with the patterns which are to be recognized, are successively inputted to the ANN. For each sample pattern there is a known appropriate output pattern, i.e. a pattern which should be produced from the network in response to that input pattern. The required known output patterns are referred to as the teaching data. In the learning procedure, the learning data patterns are successively supplied to the ANN, and resultant output patterns produced from the ANN are compared with the corresponding teaching data patterns, to obtain respective amounts of recognition error. The internal parameters of the ANN are successively adjusted in accordance with these sequentially obtained amounts of error, using a suitable learning algorithm. These operations are repetitively executed for the set of learning data, until a predetermined degree of convergence towards a maximum degree of pattern recognition is achieved (i.e., the maximum that can be achieved by using that particular set of learning data). The degree of recognition can be measured as a recognition index, expressed, for example, as a percentage.
The greater the number of sample patterns constituting the learning data, the greater will be the invariant characteristic information that is learned by the ANN. Alternatively stated, a learning algorithm which is utilized in such a procedure (i.e. for adjusting the ANN internal parameters in accordance with the error amounts obtained during the learning procedure) attempts to achieve learning of a complete set of probability distributions of a statistical population, i.e. a statistical population which consists of data, consisting of all of the possible patterns which the ANN will be required to recognize after learning has been achieved. That is to say, the learning algorithm performs a kind of pre-processing, prior to actual pattern recognition operation being started, whereby characteristics of the patterns which are to be recognized are extracted and applied to modify the internal parameters of the ANN.
In the practice of the prior art it has been necessary to utilize as large a number of sample data in the learning procedure as possible, in order to maximize the recognition index which is achieved for a ANN. However, there are practical limitations on the number of sample patterns which can be stored in memory for use as learning data. Furthermore, such learning data may include data which will actually tend to lower the recognition index, if used in the learning procedure. Accordingly, and as will be better appreciated after reading and understanding the more detailed description below, the decentralized architecture or structure of the instant new detection system and the staging of testing significantly overcomes such prior art related disadvantages.
ANNs can be divided into two classes: feed-forward and feedback neural networks. Within each class, ANNs are also characterized by the number of hidden layers, number of neurons in a given layer, and the method of learning. While many different types of learning are available, the back propagation learning algorithm (BPLA) is of the most interest to the practice of the instant invention. The BPLA is an error-correcting learning procedure which uses the gradient descent method to adjust the synaptic weights. BPLA is intended for ANNs with an input layer, any number of hidden layers, and an output layer. In the most preferred embodiments of the instant invention, the ANNs used are feed forward and possess two hidden layers. Other types of ANNs with different topologies and learning algorithms can be used as well. As will be better appreciated from the teachings and discussions found infra, the first two embodiments of the instant invention, i.e. embodiments one and two utilize ANNs to effect the desired and necessary learning and decision making for early detection of boiler tube leak events.
Fuzzy Logic. Exact models of dynamical systems become increasingly difficult to obtain if not impossible as system complexity increases. This fact is summarized by what Zadeh, infra, called the principle of incompatibility:xe2x80x9cas the complexity of a system increases, our ability to make precise and yet significant statements about its behavior diminishes until a threshold is reached beyond which precision and significance (or relevance) become almost mutually exclusive characteristics.xe2x80x9d [L. A. Zadeh, xe2x80x9cA theory of approximate reasoning,xe2x80x9d in J. Hayes, D. Michie, and L. I. Mikulich, (eds.), Machine Intelligence, Vol. 9, Halstead Press: New York, SMC-3, 1979].
The uncertainty in the knowledge about real-world systems and their dynamic models has motivated the application of fuzzy set theory to handle real world problems. [L. A. Zadeh, xe2x80x9cFuzzy algorithms,.xe2x80x9d Information and Control, Vol. 12, 1968] [D. Dubois and H. Prade, xe2x80x9cFuzzy Sets and Systems: Theory and Applications,xe2x80x9d Academic, Orlando, FL., 1980] This motivation stems from the fact that fuzzy set theory provides a suitable representation of the uncertainty in system knowledge and dynamic models. In fuzzy set theory the reasoning in the face of uncertain information, called approximate reasoning, employs fuzzy logic as a framework for uncertain information processing and inference. [R. R. Yager and D. P. Filev. xe2x80x9cEssentials of Fuzzy Modeling and Control,xe2x80x9d Wiley Interscience, New York, 1994] Fuzzy set theory is an approach useful for presenting and utilizing linguistic xe2x80x9cqualitativexe2x80x9d descriptions in computerized inference which improves the potential to model human reasoning in an inexact and uncertain domain in cases where statistical information is not available. The concept of possibility may be used to model the confidence level of various hypotheses by a number between zero and one, where one may be the highest degree of confidence and zero the lowest, or vice versa. In order to quantify inexactness, fuzzy set theory utilizes the notion of a membership function in terms of the level of confidence that a particular element belongs to a particular fuzzy set. Given the complexity of boiler tube leak events, it will be appreciated by those skilled in this art that there exists substantial motivation to utilize fuzzy logic in attempting to effect the detection of boiler tube leaks at the earliest possible moment by a technique which looks for an approximate or xe2x80x9cfuzzyxe2x80x9d map, between tube leak events and the sensitive variables, supra, and thereafter utilize approximate reasoning for detecting the occurrence and location of boiler tube leaks. Accordingly, the second two embodiments of the instant invention, i.e. embodiments three and four, integrate ANNs and fuzzy logic to effect the desired early detection of boiler tube leaks.
In the prior art, three main techniques have been proposed to detect boiler tube leaks: acoustic based systems, mass balance, and adb hoc expert systems. In the acoustic based systems taught and disclosed in U.S. Pat. No. 3,831,561, Yamamoto et al., Aug. 27, 1974; U.S. Pat. No. 4,960,079, Marziale et al., Oct. 2, 1990; U.S. Pat. No. 4,979,820, Shakkotta et al., Dec. 25, 1990; and U.S. Pat. No. 4,998,439, Shepard, Mar. 12, 1991, the principal idea is to listen, using acoustic sensors, to the sound produced by the jet of steam attendant a tube leak, or, as oftentimes herein referred to, a leak event. The technical limitation to this approach is that often times the sound produced by the tube leak is buried in the background acoustical noise of the tube environment. Accordingly, early detection of a leak is rather difficult because at the early stage of a leak background acoustical noise oftentimes masks or overrides the noise associated with the escaping steam. From a cost standpoint, the prior art technique oftentimes requires fifty or more acoustic sensors to cover the main parts of the boiler where a leak is most likely to occur. In addition, these sensors have to be maintained in proper operating condition thereby resulting in high attendant maintenance costs.
In the mass balance approach, used by some utilities, when a leak occurs there results a dependent increase in the amount of make up flow (amount of water needed to replace the loss of water due to the leak). Such an increase in make up flow is used as an indicator of the magnitude of the leak. As in the acoustic system based approach, supra, when the leak is small, the make up flow is negligible and all but impossible to discern. In addition, the mass balance approach is valid only when the boiler is operating in a steady-state operation. Most often, this particular requirement can not be met since boilers normally operate under constantly changing dynamical conditions caused by attendant control system operation and changes in load on the generating equipment utilizing the boiler output. Most important, this technique is fraught with the paramount disadvantage that even when the occurrence of a leak is detected it can not be used to locate a leak in such boiler.
The add hoc technique consists of detection of tube leaks using a so-called expert system approach. A major drawback and disadvantage of this approach is that it lacks universality. That it to say, it can only be used, after an extended time of modification of the design and tuning, to the specific boiler with which the expert person (the person who provides the rules) is familiar. Because of its add hoc nature, the development cost of such techniques can prove to be prohibitive. The lack of universality of the add hoc approach makes it even less attractive than the two prior art approaches and techniques discussed above.
The instant, new and novel approach is used to overcome the prior art problems heretofore associated with effective and early detection of boiler tube leak events and is multifaceted. For instance, at the outset of attempting to meet the principal objects of the instant invention, a dependable technique for identifying boiler tube leak sensitive variables was developed. Thereafter and once an occurrence of a tube leak event was determined, the boiler tube leak detection problem dealing with its location was solved by learning the map between such sensitive variables, the leak level, and the leak location. Such learning can be accomplished by the practice of two different mapping procedures with the map between sensitive variables and tube leak being treated as either a crisp map or a fuzzy map. In the first embodiment of the instant invention the crisp map represents a larger number of sensitive variables than does the crisp map utilized in the second embodiment of this new and novel invention. This larger number of variables represents the greater number of transducers-which are currently utilized for control of more modern boilers as opposed to the smaller number of transducers utilized in the monitoring and control of boilers of older design. In the practice of both these embodiments, i.e. one and two, ANNs were used to learn these maps via supervised training. Alternatively, in the practice of the third and fourth embodiments of the instant invention, the map between sensitive variables and a tube leak is modeled as a fuzzy map. It has been determined that fuzzy sets and fuzzy logic may conveniently be used to capture this map in the form of xe2x80x9cIf Thenxe2x80x9d fuzzy rules. The parameters of the fuzzy sets in these xe2x80x9cIf Thenxe2x80x9d rules are learned using a fuzzy ANN. Once the map is correctly learned, when future measurements of the relevant sensitive variables are input to the ANN based detection system, it will output the value of the map for the particular combination of the variables. If the value of the map is zero, no leak is present. However, once the output of the ANN is nonzero (above a given threshold) a leak is present in the boiler and its location must be identified. Accordingly, herein are described and taught four embodiments designed for detection and localization of boilers tube leaks or leak events in industrial boilers. The first two designs, or embodiments rely solely on ANNs, while the last two, through the utilization of inference engines, integrate ANNs with fuzzy logic.
It is therefore a principle object of the instant invention to provide utilities and other users of industrial boilers with a new, improved and dependable system which is capable of effecting early detection of boiler tube leaks. The instant, new and novel system, method and technique, instead of requiring additional acoustic sensors, takes advantage of existing process variables which are already in place and are being used for the purpose of boiler control and monitoring. The instant, new and novel systems can be custom tailored to any boiler without the need for input of a so-called human boiler expert. The designs taught, described, and claimed herein employ, in their more advanced development stages, two different technologies. The first and second embodiments use ANNs and the third and fourth embodiments integrate both such ANNs with fuzzy logic. In addition, the implementation of the first embodiment, supra, may be through the utilization of software designed specifically for use therein, although there presently is contemplated the VLSI implementation of this first embodiment, in the form of a VLSI chip.
Still further and more general objects and advantages of the present invention will appear from the more detailed description set forth below, it being understood, however, that this more detailed description is given by way of illustration and explanation only, and not necessarily by way of limitation since various changes therein may be made by those skilled in the art without departing from the true spirit and scope of the present invention.